Class-Aware Prototype Learning with Negative Contrast for Test-Time Adaptation of Vision-Language Models

TL;DR

This paper introduces CPL-NC, a lightweight test-time adaptation framework for vision-language models that improves class separation and handles long-tailed distributions by dynamically managing prototypes and using negative contrastive learning.

Contribution

The paper proposes CPL-NC, a novel TTA method that incorporates class-aware prototype caching and negative contrastive learning to enhance model robustness under distribution shifts.

Findings

CPL-NC outperforms previous TTA methods on 15 benchmarks.

The class-aware prototype cache effectively manages long-tailed class distributions.

Negative contrastive learning improves class separability and model accuracy.

Abstract

Vision-Language Models (VLMs) demonstrate impressive zero-shot generalization through large-scale image-text pretraining, yet their performance can drop once the deployment distribution diverges from the training distribution. To address this, Test-Time Adaptation (TTA) methods update models using unlabeled target data. However, existing approaches often ignore two key challenges: prototype degradation in long-tailed distributions and confusion between semantically similar classes. To tackle these issues, we propose \textbf{C}lass-Aware \textbf{P}rototype \textbf{L}earning with \textbf{N}egative \textbf{C}ontrast(\textbf{CPL-NC}), a lightweight TTA framework designed specifically for VLMs to enhance generalization under distribution shifts. CPL-NC introduces a \textit{Class-Aware Prototype Cache} Module that dynamically adjusts per-class capacity based on test-time frequency and activation history, with a rejuvenation mechanism for inactive classes to retain rare-category knowledge. Additionally, a \textit{Negative Contrastive Learning} Mechanism identifies and constrains hard visual-textual negatives to improve class separability. The framework employs asymmetric optimization, refining only textual prototypes while anchoring on stable visual features. Experiments on 15 benchmarks show that CPL-NC consistently outperforms prior TTA methods across both ResNet-50 and ViT-B/16 backbones.